Add to ORKGStrong consistency and asymptotic normality of the Gaussian pseudo-maximum likelihood estimate of the parameters in a wide class of ARCH(1) processes are established. We require the ARCH weights to decay at least hyperbolically, with a faster rate needed for the central limit theorem than for the law of large numbers. Various rates are illustrated in examples of particular parameteriza- tions in which our conditions are shown to be satis ed
Abstract: In this paper, we have two asymptotic objectives: the LAN and the residual empirical proce...
We consider the estimation of parametric fractional time series models in which not only is the memo...
We develop a novel asymptotic theory for local polynomial (quasi-) maximum-likelihood estimators of ...
Strong consistency and asymptotic normality of the Gaussian pseudo maximum likelihood estimate of th...
The author presents asymptotic results for the class of pseudo-likelihood estimators in the autoregr...
Linear ARCH (LARCH) processes have been introduced by Robin-son (1991) to model long-range dependenc...
This paper considers a class of finite-order autoregressive linear ARCH models. The model captures ...
In this paper we derive (weak) consistency and the asymptotic distribution of pseudo maximum likelih...
This paper studies the asymptotic properties of the quasi-maximum likelihood estimator of ARCH(1) m...
ARCH(∞) models nest a wide range of ARCH and GARCH models including models with long memory in volat...
The pseudo-marginal algorithm is a variant of the Metropolis–Hastings algorithm which samples asympt...
This paper derives asymptotic normality of a class of M-estimators in the generalized autoregressive...
We propose an estimator for parameters of nonlinear mixed effects model, obtained by maximization of...
ARCH and GARCH models directly address the dependency of conditional second moments, and have proved...
International audienceThis paper considers a class of finite-order autoregressive linear ARCH models...
Abstract: In this paper, we have two asymptotic objectives: the LAN and the residual empirical proce...
We consider the estimation of parametric fractional time series models in which not only is the memo...
We develop a novel asymptotic theory for local polynomial (quasi-) maximum-likelihood estimators of ...
Strong consistency and asymptotic normality of the Gaussian pseudo maximum likelihood estimate of th...
The author presents asymptotic results for the class of pseudo-likelihood estimators in the autoregr...
Linear ARCH (LARCH) processes have been introduced by Robin-son (1991) to model long-range dependenc...
This paper considers a class of finite-order autoregressive linear ARCH models. The model captures ...
In this paper we derive (weak) consistency and the asymptotic distribution of pseudo maximum likelih...
This paper studies the asymptotic properties of the quasi-maximum likelihood estimator of ARCH(1) m...
ARCH(∞) models nest a wide range of ARCH and GARCH models including models with long memory in volat...
The pseudo-marginal algorithm is a variant of the Metropolis–Hastings algorithm which samples asympt...
This paper derives asymptotic normality of a class of M-estimators in the generalized autoregressive...
We propose an estimator for parameters of nonlinear mixed effects model, obtained by maximization of...
ARCH and GARCH models directly address the dependency of conditional second moments, and have proved...
International audienceThis paper considers a class of finite-order autoregressive linear ARCH models...
Abstract: In this paper, we have two asymptotic objectives: the LAN and the residual empirical proce...
We consider the estimation of parametric fractional time series models in which not only is the memo...
We develop a novel asymptotic theory for local polynomial (quasi-) maximum-likelihood estimators of ...